np.sign() for sign detection in NumPy - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of np.sign() on mixed values

What is the output of the following code?

import numpy as np
arr = np.array([-3, 0, 4, -1, 2])
signs = np.sign(arr)
print(signs)

NumPy

import numpy as np
arr = np.array([-3, 0, 4, -1, 2])
signs = np.sign(arr)
print(signs)

A [-1 0 1 -1 1]

B [1 0 1 1 1]

C [-1 1 1 -1 1]

D [0 0 0 0 0]

💡 Hint

np.sign returns -1 for negative, 0 for zero, and 1 for positive values.

np.sign returns an array where each element is -1 if the original element is negative, 0 if zero, and 1 if positive. So for [-3, 0, 4, -1, 2], the output is [-1, 0, 1, -1, 1].

❓ data_output

intermediate

Count of positive, negative, and zero values using np.sign()

Given the array below, what is the count of positive, negative, and zero values using np.sign()?

import numpy as np
arr = np.array([5, -2, 0, 7, -8, 0, 3])
signs = np.sign(arr)
counts = {"positive": np.sum(signs == 1), "negative": np.sum(signs == -1), "zero": np.sum(signs == 0)}
print(counts)

NumPy

import numpy as np
arr = np.array([5, -2, 0, 7, -8, 0, 3])
signs = np.sign(arr)
counts = {"positive": np.sum(signs == 1), "negative": np.sum(signs == -1), "zero": np.sum(signs == 0)}
print(counts)

A {"positive": 3, "negative": 2, "zero": 2}

B {"positive": 4, "negative": 2, "zero": 1}

C {"positive": 3, "negative": 3, "zero": 1}

D {"positive": 2, "negative": 2, "zero": 3}

💡 Hint

Count how many elements are 1, -1, and 0 in the signs array.

The array has 3 positive numbers (5,7,3), 2 negative numbers (-2,-8), and 2 zeros (0,0). np.sign returns 1, -1, and 0 respectively, so counts match accordingly.

🔧 Debug

advanced

Identify the error in np.sign usage

What error will this code raise?

import numpy as np
arr = np.array([1, -1, 0])
signs = np.sign(arr, out=[0,0,0])
print(signs)

NumPy

import numpy as np
arr = np.array([1, -1, 0])
signs = np.sign(arr, out=[0,0,0])
print(signs)

A AttributeError: 'list' object has no attribute 'dtype'

B ValueError: shape mismatch

C TypeError: 'out' must be a ndarray

D No error, prints [1 -1 0]

💡 Hint

The 'out' parameter expects a numpy array, not a list.

The 'out' parameter in np.sign must be a numpy ndarray to store the output. Passing a Python list causes a TypeError.

🚀 Application

advanced

Using np.sign to flip signs conditionally

You want to flip the sign of all negative numbers in an array but keep positive and zero values unchanged. Which code snippet achieves this using np.sign() and numpy operations?

A

arr = np.array([-3, 0, 4, -1, 2])
result = arr * np.sign(arr)

B

arr = np.array([-3, 0, 4, -1, 2])
result = arr * (np.sign(arr) == 1) + arr * (np.sign(arr) == 0)

C

arr = np.array([-3, 0, 4, -1, 2])
result = np.abs(arr)

D

arr = np.array([-3, 0, 4, -1, 2])
result = arr * (np.sign(arr) == -1) * -1 + arr * (np.sign(arr) != -1)

💡 Hint

Use np.sign to detect negatives and multiply by -1 to flip them.

Option D flips negative values by multiplying them by -1 and keeps others unchanged. Option D converts all to positive, which is not the same as flipping only negatives. Options B and D do not flip negatives correctly.

🧠 Conceptual

expert

Behavior of np.sign with floating point edge cases

What is the output of np.sign() when applied to the array containing special floating point values?

import numpy as np
arr = np.array([-0.0, 0.0, np.nan, np.inf, -np.inf])
signs = np.sign(arr)
print(signs)

NumPy

import numpy as np
arr = np.array([-0.0, 0.0, np.nan, np.inf, -np.inf])
signs = np.sign(arr)
print(signs)

A [-0. 0. nan 1. -1.]

B [0. 0. nan 1. -1.]

C [-1. 0. nan 1. -1.]

D [0. 0. 0. 1. -1.]

💡 Hint

np.sign returns 0 for both +0.0 and -0.0, and nan for np.nan.

np.sign returns 0 for both +0.0 and -0.0 (no negative zero in output), nan for np.nan, 1 for positive infinity, and -1 for negative infinity.